Angela Yu Course 100 Days of Code:

The Complete Python Pro Bootcamp

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Day 99, Assignment 18:

Data Science: "Analyse and Visualise the Space Race"

Modified version of lesson plan with updated scraped data from dataset and other changes specified in README.md

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Introduction

This dataset was scraped from nextspaceflight.com.

It includes all the space missions since the beginning of Space Race between the USA and the Soviet Union in 1957!

Install Package with Country Codes

pip install iso3166

Upgrade Plotly

Run the cell below if you are working with Google Colab.

pip install --upgrade plotly

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Import Statements

import numpy as np
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
import seaborn as sns

# These might be helpful:
from iso3166 import countries
from datetime import datetime, timedelta

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Notebook Presentation

pd.options.display.float_format = '{:,.2f}'.format

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ANB: I Scraped the Most Recent Data...

...and updated the CSV.

This first entry is a few years old and is on page 24:

https://nextspaceflight.com/launches/past/?page=24&search=

I'll scrape and update from the website's pages 1-24.

In the CSV file see the necessary data:

csv
,Unnamed: 0,Organisation,Location,Date,Detail,Rocket_Status,Price,Mission_Status
4323,4323,RVSN USSR,"Site 1/5, Baikonur Cosmodrome, Kazakhstan","Fri Oct 04, 1957 19:28 UTC",Sputnik 8K71PS | Sputnik-1,StatusRetired,,Success

csv
,Unnamed: 0,Organisation,Location,Date,Detail,Rocket_Status,Price,Mission_Status
0,0,SpaceX,"LC-39A, Kennedy Space Center, Florida, USA","Fri Aug 07, 2020 05:12 UTC",Falcon 9 Block 5 | Starlink V1 L9 & BlackSky,StatusActive,50.0,Success

I used Selenium for the scrape then Pandas to integrate w/Angela's CSV.

See the project scrapeStuff folder for all the details...

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Load the Data

df_data = pd.read_csv('mission_launches.csv')

df_data.head(5)

	Unnamed: 0	Organisation	Location	Date	Detail	Rocket_Status	Price	Mission_Status
0	0	SpaceX	SLC-40, Cape Canaveral SFS, Florida, USA	Sun Apr 28, 2024 6:08 PM EDT	Falcon 9 Block 5 | Starlink Group 6-54	StatusActive	67.0	Success
1	1	SpaceX	LC-39A, Kennedy Space Center, Florida, USA	Sat Apr 27, 2024 8:34 PM EDT	Falcon 9 Block 5 | Galileo FOC FM25 & FM27	StatusActive	67.0	Success
2	2	CASC	Site 901 (SLS-1), Jiuquan Satellite Launch Cen...	Thu Apr 25, 2024 8:59 AM EDT	Long March 2F/G | Shenzhou 18	StatusActive	NaN	Success
3	3	Rocket Lab	Rocket Lab LC-1B, Mahia Peninsula, New Zealand	Tue Apr 23, 2024 6:32 PM EDT	Electron/Curie | Beginning Of The Swarm	StatusActive	7.5	Success
4	4	SpaceX	SLC-40, Cape Canaveral SFS, Florida, USA	Tue Apr 23, 2024 6:17 PM EDT	Falcon 9 Block 5 | Starlink Group 6-53	StatusActive	67.0	Success

For clarity, rename 'Price' to 'Price_US_Mil`

df_data.rename(columns={"Price": "Price_US_Mil"}, inplace=True)

df_data

	Unnamed: 0	Organisation	Location	Date	Detail	Rocket_Status	Price_US_Mil	Mission_Status
0	0	SpaceX	SLC-40, Cape Canaveral SFS, Florida, USA	Sun Apr 28, 2024 6:08 PM EDT	Falcon 9 Block 5 | Starlink Group 6-54	StatusActive	67.0	Success
1	1	SpaceX	LC-39A, Kennedy Space Center, Florida, USA	Sat Apr 27, 2024 8:34 PM EDT	Falcon 9 Block 5 | Galileo FOC FM25 & FM27	StatusActive	67.0	Success
2	2	CASC	Site 901 (SLS-1), Jiuquan Satellite Launch Cen...	Thu Apr 25, 2024 8:59 AM EDT	Long March 2F/G | Shenzhou 18	StatusActive	NaN	Success
3	3	Rocket Lab	Rocket Lab LC-1B, Mahia Peninsula, New Zealand	Tue Apr 23, 2024 6:32 PM EDT	Electron/Curie | Beginning Of The Swarm	StatusActive	7.5	Success
4	4	SpaceX	SLC-40, Cape Canaveral SFS, Florida, USA	Tue Apr 23, 2024 6:17 PM EDT	Falcon 9 Block 5 | Starlink Group 6-53	StatusActive	67.0	Success
...	...	...	...	...	...	...	...	...
5022	5022	US Navy	LC-18A, Cape Canaveral AFS, Florida, USA	Wed Feb 05, 1958 07:33 UTC	Vanguard | Vanguard TV3BU	StatusRetired	NaN	Failure
5023	5023	AMBA	LC-26A, Cape Canaveral AFS, Florida, USA	Sat Feb 01, 1958 03:48 UTC	Juno I | Explorer 1	StatusRetired	NaN	Success
5024	5024	US Navy	LC-18A, Cape Canaveral AFS, Florida, USA	Fri Dec 06, 1957 16:44 UTC	Vanguard | Vanguard TV3	StatusRetired	NaN	Failure
5025	5025	RVSN USSR	Site 1/5, Baikonur Cosmodrome, Kazakhstan	Sun Nov 03, 1957 02:30 UTC	Sputnik 8K71PS | Sputnik-2	StatusRetired	NaN	Success
5026	5026	RVSN USSR	Site 1/5, Baikonur Cosmodrome, Kazakhstan	Fri Oct 04, 1957 19:28 UTC	Sputnik 8K71PS | Sputnik-1	StatusRetired	NaN	Success

5027 rows × 8 columns

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Preliminary Data Exploration

• What is the shape of df_data?
• How many rows and columns does it have?
• What are the column names?
• Are there any NaN values or duplicates?

df_shape = df_data.shape
rows, columns = df_shape

print(f"`df_data` has a shape of {df_shape}.")
print(f"Therefore it has {rows} rows and {columns} colunns.")

`df_data` has a shape of (5027, 8).
Therefore it has 5027 rows and 8 colunns.

col_names = ", ".join(df_data.columns)

print(f"The column names are {col_names}.")

The column names are Unnamed: 0, Organisation, Location, Date, Detail, Rocket_Status, Price_US_Mil, Mission_Status.

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Data Cleaning - Check for Missing Values and Duplicates

Consider removing columns containing junk data.

ANB: See below (just after Descriptive Statistics section) for continued data cleaning....

ANB: There is an extra column duplicating the index called "Unnamed: 0". I'll remove that:

df_data = df_data.drop("Unnamed: 0", axis=1)

df_shape = df_data.shape
rows, columns = df_shape

print(f"`df_data` has a shape of {df_shape}.")
print(f"Therefore it has {rows} rows and {columns} colunns.")

`df_data` has a shape of (5027, 7).
Therefore it has 5027 rows and 7 colunns.

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ANB: Convert and make uniform Date values:

ANB - I'm taking this opportunity to convert the Date column to pd.to_datetime() so we can parse the min/max, etc...

First I'll need to get all the EDTs to be UTCs.

Then, convert to DT objects.

Here's the function:

import pytz

# Convert EDT times to UTC:


def convert_edt_to_utc(date_str):
    if 'EDT' in date_str or 'EST' in date_str:
        date_str = date_str.replace('EDT', '').replace('EST', '').strip()
        dt = pd.to_datetime(date_str)
        edt_tz = pytz.timezone('US/Eastern')
        dt_utc = edt_tz.localize(dt).astimezone(pytz.utc)
        return dt_utc
    else:
        return pd.to_datetime(date_str)


df_data['Date'] = df_data['Date'].apply(convert_edt_to_utc)

# # Inspect `df_data.Date`:
# for i in df_data.Date:
#     print(i)

Some vals look different:

1958-09-23 07:40:00+00:00 1958-08-29 00:00:00

Let's address this:

def convert_to_utc(dt_obj):
    if dt_obj.tzinfo:
        return dt_obj.astimezone(pytz.utc)
    else:
        return pytz.utc.localize(dt_obj)

df_data.Date = df_data.Date.apply(convert_to_utc)

# # Reinspect:
# for i in df_data.Date:
#     print(i)

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ANB: Convert Price values to nums:

# # Inspect the column:
# for i in df_data.Price_US_Mil:
#     print(i)

This is returning items like 1,160.

Since this column indicates millions, that would mean 1.16 billion.

So let's just remove the commas before type conversion:

def remove_commas_and_make_float(price_entry):
    if type(price_entry) is str:
        if ',' in price_entry:
            price_entry = price_entry.replace(',', '')
        return float(price_entry)
    else:
        return price_entry

df_data.Price_US_Mil = df_data.Price_US_Mil.apply(remove_commas_and_make_float)

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Descriptive Statistics

Now the descriptive statistics:

df_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5027 entries, 0 to 5026
Data columns (total 7 columns):
 #   Column          Non-Null Count  Dtype              
---  ------          --------------  -----              
 0   Organisation    5027 non-null   object             
 1   Location        5027 non-null   object             
 2   Date            5027 non-null   datetime64[ns, UTC]
 3   Detail          5027 non-null   object             
 4   Rocket_Status   5027 non-null   object             
 5   Price_US_Mil    1556 non-null   float64            
 6   Mission_Status  5027 non-null   object             
dtypes: datetime64[ns, UTC](1), float64(1), object(5)
memory usage: 275.0+ KB

df_data.describe()

	Price_US_Mil
count	1,556.00
mean	115.67
std	234.63
min	2.50
25%	30.80
50%	64.68
75%	97.00
max	5,000.00

df_data.Date.max()

Timestamp('2024-04-28 22:08:00+0000', tz='UTC')

df_data.Date.min()

Timestamp('1957-10-04 19:28:00+0000', tz='UTC')

df_data.Price_US_Mil.describe()

count   1,556.00
mean      115.67
std       234.63
min         2.50
25%        30.80
50%        64.68
75%        97.00
max     5,000.00
Name: Price_US_Mil, dtype: float64

df_data.Date.describe()

count                                   5027
mean     1992-10-09 15:35:29.468868096+00:00
min                1957-10-04 19:28:00+00:00
25%                1974-02-01 20:00:00+00:00
50%                1990-07-25 19:21:00+00:00
75%                2014-07-01 07:09:00+00:00
max                2024-04-28 22:08:00+00:00
Name: Date, dtype: object

# # Inspect the column:
# for i in df_data.Price_US_Mil:
#     print(i)

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ANB: More Data Cleaning - Check for Missing Values and Duplicates

Check null vals:

df_data.isnull().describe()

	Organisation	Location	Date	Detail	Rocket_Status	Price_US_Mil	Mission_Status
count	5027	5027	5027	5027	5027	5027	5027
unique	1	1	1	1	1	2	1
top	False	False	False	False	False	True	False
freq	5027	5027	5027	5027	5027	3471	5027

df_data.isna().sum()

Organisation         0
Location             0
Date                 0
Detail               0
Rocket_Status        0
Price_US_Mil      3471
Mission_Status       0
dtype: int64

Price has 5012 nan vals.

df_data.notna().sum()

Organisation      5027
Location          5027
Date              5027
Detail            5027
Rocket_Status     5027
Price_US_Mil      1556
Mission_Status    5027
dtype: int64

Every other column has no null vals. I think we're good to go on that score.

Check duplicates:

df_data.duplicated().sum()

1

Remove all duplicates in-place:

df_data.drop_duplicates(inplace=True)

df_data.duplicated().sum()

0

Duplicates removed.

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Number of Launches per Company

Create a chart that shows the number of space mission launches by organisation.

Create series with value counts from df_data["Organisation]:

series_organisation = df_data.Organisation.value_counts()

series_organisation

Organisation
RVSN USSR           1777
CASC                 427
SpaceX               343
Arianespace          299
General Dynamics     251
                    ... 
OrienSpace             1
Space Pioneer          1
Khrunichev             1
Sandia                 1
EER                    1
Name: count, Length: 69, dtype: int64

import numpy as np
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
import seaborn as sns
from iso3166 import countries
from datetime import datetime, timedelta

I tried pie charts but they're too congested...

plt.figure(figsize=(15, 15), dpi=120)
plt.title("Space Mission Launches by Organisation")

plt.pie(series_organisation, labels=series_organisation.index)

plt.show()

fig = px.pie(
    names=series_organisation.index,
    values=series_organisation,
    labels=series_organisation.index,
    height=1000,
    width=1000,
)

fig.update_layout(
    title=dict(text="Missions by Organisation", font=dict(
        size=24), automargin=True, yref='paper'),
)


# fig.update_traces(textposition='outside', textinfo='percent+label')
fig.update_traces(textposition='inside', textinfo='percent+label')

fig.show()

So trying bar chart instead...

import matplotlib.colors as mcolors
from random import choices

css_colors = list(mcolors.CSS4_COLORS.keys())

bar_colors = choices(css_colors, k=len(series_organisation))

fig, ax = plt.subplots(figsize=(30, 15))

ax.bar(series_organisation.index,
       series_organisation,
       label=series_organisation.index,
       color=bar_colors)

ax.set_xlabel("Organisation Name")
ax.set_ylabel("Number of Launches")
ax.set_title("Space Mission Launches by Organisation")
ax.legend(title="Organisation")

sns.set_theme(style='white')

plt.legend(ncol=10)
plt.xticks(rotation=45)
plt.show()

This plot has values too varied for clear visual representation of smaller values, so break into four plots. Consider creating lists with more efficient algorithm:

len_4 = len(series_organisation) // 4

names_1 = series_organisation.index[:len_4]
vals_1 = series_organisation[:len_4]
colors_1 = choices(css_colors, k=len(vals_1))

names_2 = series_organisation.index[len_4:len_4 * 2]
vals_2 = series_organisation[len_4:len_4 * 2]
colors_2 = choices(css_colors, k=len(vals_2))

names_3 = series_organisation.index[len_4 * 2:len_4 * 3]
vals_3 = series_organisation[len_4 * 2:len_4 * 3]
colors_3 = choices(css_colors, k=len(vals_3))

names_4 = series_organisation.index[len_4 * 3:]
vals_4 = series_organisation[len_4 * 3:]
colors_4 = choices(css_colors, k=len(vals_4))

fig, ax = plt.subplots(figsize=(14, 7), dpi=120)

ax.bar(names_1,
       vals_1,
       label=names_1,
       color=colors_1)

ax.set_title("Space Mission Launches by Organisation 1/4", fontsize=24)
ax.set_xlabel("Organisation Name", fontsize=18)
ax.set_ylabel("Number of Launches", fontsize=18)
ax.legend(title="Organisation")

plt.legend(ncol=2)
plt.xticks(rotation=90)
plt.show()

fig, ax = plt.subplots(figsize=(14, 7), dpi=120)

ax.bar(names_2,
       vals_2,
       label=names_2,
       color=colors_2)

ax.set_title("Space Mission Launches by Organisation 2/4", fontsize=24)
ax.set_xlabel("Organisation Name", fontsize=18)
ax.set_ylabel("Number of Launches", fontsize=18)
ax.legend(title="Organisation")

plt.legend(ncol=2)
plt.xticks(rotation=90)
plt.show()

fig, ax = plt.subplots(figsize=(14, 7), dpi=120)

ax.bar(names_3,
       vals_3,
       label=names_3,
       color=colors_3)

ax.set_title("Space Mission Launches by Organisation 3/4", fontsize=24)
ax.set_xlabel("Organisation Name", fontsize=18)
ax.set_ylabel("Number of Launches", fontsize=18)
ax.legend(title="Organisation")

plt.legend(ncol=2)
plt.xticks(rotation=90)
plt.show()

fig, ax = plt.subplots(figsize=(14, 7), dpi=120)

ax.bar(names_4,
       vals_4,
       label=names_4,
       color=colors_4)

ax.set_title("Space Mission Launches by Organisation 4/4", fontsize=24)
ax.set_xlabel("Organisation Name", fontsize=18)
ax.set_ylabel("Number of Launches", fontsize=18)
ax.legend(title="Organisation")

plt.legend(ncol=2)
plt.xticks(rotation=90)
plt.show()

That looks good for now!

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Number of Active versus Retired Rockets

How many rockets are active compared to those that are decomissioned?

rocket_status_value_counts = df_data.Rocket_Status.value_counts()

retired_val, active_val = rocket_status_value_counts.tolist()
retired_label, active_label = rocket_status_value_counts.index.tolist()

print(f"Number of {retired_label}: {retired_val}.")
print(f"Number of {active_label}: {active_val}.")

Number of StatusRetired: 3569.
Number of StatusActive: 1457.

# Simplest plot:
plt.figure(figsize=(7, 7))
plt.title("Active versus Retired Rockets")

plt.pie(rocket_status_value_counts.tolist(),
        labels=rocket_status_value_counts.index.tolist())

plt.legend(rocket_status_value_counts.tolist())
plt.show()

# More descriptive plot:
fix, ax = plt.subplots(figsize=(7, 7))

ax.pie(rocket_status_value_counts.tolist(),
       labels=rocket_status_value_counts.index.tolist(),
       autopct='%.1f%%')

ax.set_title("Active versus Retired Rockets")
plt.legend(rocket_status_value_counts.tolist())
plt.show()

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Distribution of Mission Status

How many missions were successful? How many missions failed?

mission_statuses = df_data.Mission_Status.value_counts()

for i in range(len(mission_statuses)):
    result = mission_statuses.index.tolist()[i]
    num = mission_statuses.tolist()[i]
    print(f"{num} missions: {result}.")

4513 missions: Success.
369 missions: Failure.
106 missions: Partial Failure.
34 missions: Suborbital.
4 missions: Prelaunch Failure.

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How Expensive are the Launches?

Create a histogram and visualise the distribution.

The price column is given in USD millions (careful of missing values).

price_nan_num = df_data.Price_US_Mil.isna().sum()
price_valid_num = len(df_data.Price_US_Mil) - price_nan_num
print(f"`df_data` has {
      price_valid_num}/{len(df_data.Price_US_Mil)} entries for Price.")

`df_data` has 1555/5026 entries for Price.

price_series = df_data.Price_US_Mil.dropna()

# Or try:
# price_mask = pd.notna(df_data.Price)
# price_series = df_data[price_mask]

# price_series = df_data.loc[df_data.Price.notna(), 'Price']

w/Plotly Express histogram():

price_histogram = px.histogram(
    x=price_series,
    opacity=0.75,
    marginal='box',
    nbins=35,
    barmode='overlay',
    width=750,
    height=500
)

price_histogram.update_layout(
    title="Histogram: Distribution of Rocket Prices",
    xaxis_title="Price: $Millions US",
    yaxis_title="Count",
    coloraxis_showscale=False
)

price_histogram.show()

w/Seaborn histplot():

price_series_skew = np.round(price_series.skew(), 3)
price_series_mean = np.round(price_series.mean(), 3)

fig, ax = plt.subplots(figsize=(9, 7), dpi=120)

sns.histplot(price_series,
             kde=True,
             color='darkorange',
             legend=False,
             #  stat='density',
             ax=ax,
             alpha=0.5,
             bins=35)

sns.set_theme(style='whitegrid')

plt.title(f"Price Series\nskew: {price_series_skew}\nmean: {price_series_mean}")
plt.xlabel("Price in Millions", fontsize=12)
plt.ylabel("Count", fontsize=12)

plt.legend(labels=['KDE', 'Histogram'])

plt.show()

Compare w/param log_scale=True:

price_series_skew = np.round(price_series.skew(), 3)
price_series_mean = np.round(price_series.mean(), 3)

fig, ax = plt.subplots(figsize=(9, 7), dpi=120)

sns.histplot(price_series,
             kde=True,
             color='darkorange',
             legend=False,
             #  stat='density',
             ax=ax,
             alpha=0.5,
             log_scale=True,
             bins=35)

sns.set_theme(style='whitegrid')

plt.title(f"Price Series (Logarithmic Scale)\nskew: {price_series_skew}\nmean: {price_series_mean}")
plt.xlabel("Price: $Millions US", fontsize=12)
plt.ylabel("Count", fontsize=12)

plt.legend(labels=['KDE', 'Histogram'])

plt.show()

---

---

Use a Choropleth Map to Show the Number of Launches by Country

• Create a choropleth map using the plotly documentation

• Experiment with plotly's available colours. I quite like the sequential colour matter on this map.

• You'll need to extract a country feature as well as change the country names that no longer exist.

Wrangle the Country Names

You'll need to use a 3 letter country code for each country. You might have to change some country names.

• Russia is the Russian Federation

• New Mexico should be USA

• Yellow Sea refers to China

• Shahrud Missile Test Site should be Iran

• Pacific Missile Range Facility should be USA

• Barents Sea should be Russian Federation

• Gran Canaria should be USA

You can use the iso3166 package to convert the country names to Alpha3 format.

# # Background work:
# for i in countries:
#     print(i.name)
#     print(i.alpha2)
#     print(i.alpha3)
#     print(i.numeric)
#     print(i.apolitical_name)
#     print()

Research indicates that Sea Launch, indicated in the country_rename dict as "Pacific Ocean", had majority ownership by Russian interests, so I'm using RUS for that agency.

country_rename is a dict used to get country names that can then be converted succesfully in function get_country_names which references iso3166.countries.

get_country_names also accesses countries_iso3166_dict, which is used to achieve the final step of getting the 3-letter codes.

Create a list country_names while executing function get_country_names for clearer viewing of names when hovering over choropleth:

country_rename = {
    "Russia": "Russian Federation",
    "New Mexico": "USA",
    "Yellow Sea": "China",
    "Shahrud Missile Test Site": "Iran, Islamic Republic of",
    "Pacific Missile Range Facility": "USA",
    "Pacific Ocean": "USA",
    "Barents Sea": "Russian Federation",
    "Gran Canaria": "USA",
    "China Coastal Waters": "China",
    "United Kingdom": "United Kingdom of Great Britain and Northern Ireland",
    "North Korea": "Korea, Democratic People's Republic of",
    "South Korea": "Korea, Republic of",
    "Iran": "Iran, Islamic Republic of",
}

countries_iso3166_dict = {}
for c in countries:
    countries_iso3166_dict[c.name] = c.alpha3
    countries_iso3166_dict[c.alpha2] = c.alpha3
    countries_iso3166_dict[c.alpha3] = c.alpha3
    countries_iso3166_dict[c.numeric] = c.alpha3
    countries_iso3166_dict[c.apolitical_name] = c.alpha3


def get_country_codes(location):
    country_name = location.split(",")[-1].strip()
    country_name = country_rename.get(country_name, country_name)
    countries_alpha3 = countries_iso3166_dict.get(country_name, None)
    return countries_alpha3

Create series_country, which will list country code for each launch in df_data:

series_country_codes = df_data.Location.apply(get_country_codes)

series_country_codes.unique()

array(['USA', 'CHN', 'NZL', 'RUS', 'KAZ', 'JPN', 'IND', 'IRN', 'KOR',
       'PRK', 'FRA', 'ISR', 'GBR', 'BRA', 'KEN', 'AUS'], dtype=object)

country_value_counts series allows us to see launch count for each country code:

country_launch_value_counts = series_country_codes.value_counts()

Create lists to facilitate smooth creation of DataFrame for country codes/counts:

country_launch_counts = [i for i in country_launch_value_counts]
country_codes = country_launch_value_counts.index

Create list of country names to add to data_country so hovering on choropleth will say the country name, not just the code:

country_name_dict = {}

for c in countries:
    country_name_dict[c.alpha3] = c.name

country_names = [country_name_dict[i] for i in country_codes]

country_names

['United States of America',
 'Russian Federation',
 'Kazakhstan',
 'China',
 'France',
 'Japan',
 'India',
 'New Zealand',
 'Iran, Islamic Republic of',
 'Israel',
 'Kenya',
 "Korea, Democratic People's Republic of",
 'Korea, Republic of',
 'Australia',
 'Brazil',
 'United Kingdom of Great Britain and Northern Ireland']

data_country = {"ISO_Code": country_codes,
                "Launches": country_launch_counts,
                "Country": country_names}

df_country = pd.DataFrame(data=data_country)

df_country

	ISO_Code	Launches	Country
0	USA	1703	United States of America
1	RUS	1440	Russian Federation
2	KAZ	732	Kazakhstan
3	CHN	487	China
4	FRA	324	France
5	JPN	137	Japan
6	IND	93	India
7	NZL	43	New Zealand
8	IRN	21	Iran, Islamic Republic of
9	ISR	12	Israel
10	KEN	9	Kenya
11	PRK	8	Korea, Democratic People's Republic of
12	KOR	7	Korea, Republic of
13	AUS	6	Australia
14	BRA	3	Brazil
15	GBR	1	United Kingdom of Great Britain and Northern I...

color_scales = px.colors.named_colorscales()

from random import choice

random_color = choice(color_scales)

I experimented with color_continuous_scale=random_color.

Ended up liking 'rainbow'.

fig = px.choropleth(
    df_country,
    locations="ISO_Code",
    color="Launches",
    hover_name="Country",
    color_continuous_scale="rainbow",
    # width=1920,
    # height=1080,
)

fig.update_layout(
    title_text="Rocket Launches by Country (Choropleth Map)",
    title_font_size=20,
    title_font_family="Courier New",
    title_xref="paper",
)

fig.show()

---

---

Use a Choropleth Map to Show the Number of Failures by Country

Add a country code column to original DF for ease of reference:

df_data.insert(2, "ISO_Code", series_country_codes)

df_data.sample()

	Organisation	Location	ISO_Code	Date	Detail	Rocket_Status	Price_US_Mil	Mission_Status
2567	NASA	LC-39B, Kennedy Space Center, Florida, USA	USA	1989-10-18 16:53:00+00:00	Space Shuttle Atlantis | STS-34R	StatusRetired	450.00	Success

Also add a country name column to original DF for ease of reference:

def get_country_names(iso_code):
    country_name = country_name_dict.get(iso_code)
    return country_name


series_country_names = df_data.ISO_Code.apply(get_country_names)

df_data.insert(2, "Country_Name", series_country_names)

df_data.sample()

	Organisation	Location	Country_Name	ISO_Code	Date	Detail	Rocket_Status	Price_US_Mil	Mission_Status
3840	RVSN USSR	Site 43/4, Plesetsk Cosmodrome, Russia	Russian Federation	RUS	1973-06-06 11:30:00+00:00	Voskhod | Cosmos 563	StatusRetired	NaN	Success

See what status there are:

series_mission_status = df_data["Mission_Status"].value_counts()
series_mission_status

Mission_Status
Success              4513
Failure               369
Partial Failure       106
Suborbital             34
Prelaunch Failure       4
Name: count, dtype: int64

Consider a df for each status to map each case:

df_failure = df_data.loc[df_data["Mission_Status"] == "Failure"]

# df_success = df_data.loc[df_data["Mission_Status"] == "Success"]
# df_partial_failure = df_data.loc[df_data["Mission_Status"] == "Partial_Failure"]
# df_suborbital = df_data.loc[df_data["Mission_Status"] == "Suborbital"]
# df_prelaunch_failure = df_data.loc[df_data["Mission_Status"] == "Prelaunch Failure"]

df_by_failure = df_failure.groupby(["ISO_Code"])["Mission_Status"].count(
).reset_index(name="Count").sort_values(["Count"])

Add column w/Country Names for clarity when hovering:

df_by_failure_country_names = df_by_failure.ISO_Code.apply(get_country_names)

df_by_failure.insert(1, "Country_Name", df_by_failure_country_names)

Choropleth Mapping Launch Failures:

random_color = choice(color_scales)

fig = px.choropleth(
    df_by_failure,
    locations="ISO_Code",
    color="Count",
    hover_name="Country_Name",
    color_continuous_scale="rainbow",
)

fig.update_layout(
    title_text=f"Mission Failures Choropleth ({random_color})",
    title_font_size=20,
    title_font_family="Courier New",
    title_xref="paper",
)

fig.show()

---

---

Create a Plotly Sunburst Chart of the countries, organisations, and mission status.

---

Background on groupby() and px.sunburst():

df_doc_animals = pd.DataFrame({
    'Species': ['Primate', 'Primate', 'Bird', 'Bird', 'Bird', 'Bird'],
    'Animal': ['Gorilla', 'Human', 'Falcon', 'Falcon', 'Parrot', 'Parrot'],
    'Max Speed': [25., 17., 380., 370., 24., 24.],
    'Names': ['Golliver', 'Bruna', 'Filbo', 'Jed', 'Filba', 'Ruina'],
})

df_doc_animals

	Species	Animal	Max Speed	Names
0	Primate	Gorilla	25.00	Golliver
1	Primate	Human	17.00	Bruna
2	Bird	Falcon	380.00	Filbo
3	Bird	Falcon	370.00	Jed
4	Bird	Parrot	24.00	Filba
5	Bird	Parrot	24.00	Ruina

df_doc_animals = df_doc_animals.groupby(['Animal', 'Max Speed'])

df_doc_animals

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001C5BB6E0320>

df_doc_animals = df_doc_animals.size()

df_doc_animals

Animal   Max Speed
Falcon   370.00       1
         380.00       1
Gorilla  25.00        1
Human    17.00        1
Parrot   24.00        2
dtype: int64

df_doc_animals = df_doc_animals.reset_index(name='count')

df_doc_animals

	Animal	Max Speed	count
0	Falcon	370.00	1
1	Falcon	380.00	1
2	Gorilla	25.00	1
3	Human	17.00	1
4	Parrot	24.00	2

burst_animals = px.sunburst(df_doc_animals,
                            path=["Animal",
                                  "Max Speed"],
                            values="count",
                            title="Animals Sunburst: Animal -> Max Speed")

burst_animals.update_layout(xaxis_title="Animal",
                            yaxis_title="Max Speed",
                            coloraxis_showscale=False,
                            width=450,
                            height=450)

burst_animals.show()

The Task:

Break the process into steps:

# Group DF "Country_Name"/"Organisation"/"Mission_Status":
country_org_status = df_data.groupby(by=["Country_Name",
                                         "Organisation",
                                         "Mission_Status"])

# Calculate the size of each group/the count of occurrences for each unique combination
#  and assign as a Series:
country_org_status = country_org_status.size()

# Reset index of the resulting Series/rename the count column to 'count' &
#  rebuild the DF:
country_org_status = country_org_status.reset_index(name='count')

country_org_status = df_data.groupby(by=["Country_Name",
                                         "Organisation",
                                         "Mission_Status"]).size().reset_index(name='count')

burst = px.sunburst(country_org_status,
                    path=["Country_Name",
                          "Organisation",
                          "Mission_Status"],
                    values="count",
                    title="Space Launches Sunburst: Countries/Agencies/Mission Status")

burst.update_layout(xaxis_title="Mission Status",
                    yaxis_title="Organisation",
                    coloraxis_showscale=False,
                    width=750,
                    height=750)

burst.show()

---

---

Analyse the Total Amount of Money Spent by Organisations on Space Missions

df_org_price = df_data.filter(["Organisation", "Price_US_Mil"], axis=1)

len(df_org_price)

5026

Remove entries/rows without entries in Price column:

df_org_price = df_org_price[df_org_price["Price_US_Mil"].notna()]

df_org_price

	Organisation	Price_US_Mil
0	SpaceX	67.00
1	SpaceX	67.00
3	Rocket Lab	7.50
4	SpaceX	67.00
5	CASC	29.75
...	...	...
4558	US Air Force	59.00
4674	US Air Force	63.23
4696	US Air Force	63.23
4703	US Air Force	63.23
4723	US Air Force	63.23

1555 rows × 2 columns

df_org_price.describe()

	Price_US_Mil
count	1,555.00
mean	115.72
std	234.69
min	2.50
25%	30.80
50%	64.68
75%	97.00
max	5,000.00

len(df_org_price)

1555

df_org_spending = df_org_price.groupby(["Organisation"])["Price_US_Mil"].sum()
df_org_spending = df_org_spending.reset_index(name="Spending_US_Mil")

bar_colors = choices(css_colors, k=len(df_org_spending))

Plot df_org_spending:

fig, ax = plt.subplots(figsize=(20, 30), dpi=120)

ax.bar(df_org_spending['Organisation'],
       df_org_spending['Spending_US_Mil'],
       label=df_org_spending['Organisation'],
       color=bar_colors)

ax.set_title("Space Agencies Total Spending: $Millions US", fontsize=24)
ax.set_xlabel('Space Agency', fontsize=18)
ax.set_ylabel('Space Missions: $Millions US', fontsize=18)
ax.legend(title="Agencies")

ax.legend(ncol=5)
plt.xticks(rotation=90)
plt.show()

We need to break df_org_spending into chunks, since the range of values makes it hard to see the data clearly in the plot...

Create four subplots:

color_list = ['black', 'blue', 'brown', 'chocolate', 'cornflowerblue', 'crimson',
              'darkblue', 'darkcyan', 'darkgoldenrod', 'darkgreen', 'darkkhaki',
              'darkmagenta', 'darkolivegreen', 'darkorange', 'darkorchid', 'darkred',
              'darksalmon', 'darkseagreen', 'darkslateblue', 'darkslategray',
              'darkslategrey', 'darkturquoise', 'darkviolet', 'deeppink', 'dimgray',
              'dodgerblue', 'firebrick', 'forestgreen', 'goldenrod', 'green', 
              'greenyellow', 'indigo', 'lightgreen', 'limegreen', 'maroon', 'mediumblue', 
              'mediumseagreen', 'mediumslateblue', 'mediumspringgreen', 'midnightblue', 
              'navy', 'olive', 'olivedrab', 'palegreen', 'peru', 'purple',
              'rebeccapurple', 'rosybrown', 'royalblue', 'saddlebrown', 'sandybrown', 
              'seagreen', 'sienna', 'slateblue', 'springgreen', 'steelblue', 'yellowgreen']

df_org_spending_01 = df_org_spending[df_org_spending["Spending_US_Mil"] >= 7500]

df_org_spending_02 = df_org_spending[(df_org_spending["Spending_US_Mil"] < 7500) & (
    df_org_spending["Spending_US_Mil"] >= 1250)]

df_org_spending_03 = df_org_spending[(df_org_spending["Spending_US_Mil"] < 1250) & (
    df_org_spending["Spending_US_Mil"] >= 200)]

df_org_spending_04 = df_org_spending[(df_org_spending["Spending_US_Mil"] < 200) & (
    df_org_spending["Spending_US_Mil"] >= 20)]

df_org_spending_05 = df_org_spending[df_org_spending["Spending_US_Mil"] < 20]

spending_ranges = [(df_org_spending_01, "High Spending"),
                   (df_org_spending_02, "Medium-High Spending"),
                   (df_org_spending_03, "Medium Spending"),
                   (df_org_spending_04, "Medium-Low Spending"),
                   (df_org_spending_05, "Low Spending")]

fig, axes = plt.subplots(nrows=len(spending_ranges), figsize=(29, 23))

for (df, title), ax in zip(spending_ranges, axes):
    ax.bar(df["Organisation"],
           df["Spending_US_Mil"],
           color=choices(color_list, k=len(df)))
    ax.set_title(title)

plt.subplots_adjust(
    bottom=0.1,
    top=0.9,
    hspace=0.5,
)

plt.suptitle(t="Space Agency Spending on Rocket Launches 1957-2024\n1 Unit: $1,000,000 (US)",
             size=24, weight="bold", color="k")

plt.rcParams.update({
    "grid.color": "gainsboro",
    "axes.facecolor": "mintcream",
})

plt.show()

---

---

Analyse the Amount of Money Spent by Organisation per Launch

Now, reference the earlier df_org_price in creating a DataFrame with the average spending per launch:

Before, we got the total spending this w/groupby().sum().

df_org_price = df_data.filter(["Organisation", "Price"], axis=1)
df_org_price = df_org_price[df_org_price["Price"].notna()]

df_org_spending = df_org_price.groupby(["Organisation"])["Price"].sum()
df_org_spending = df_org_spending.reset_index(name="Spending")

We'll try a different approach for getting the average...

df_org_spending_mean = df_org_price.groupby(
    ["Organisation"])["Price_US_Mil"].mean()

df_org_spending_mean = df_org_spending_mean.reset_index(
    name="Average_Spending")

df_org_spending_mean.describe()

	Average_Spending
count	35.00
mean	199.94
std	840.16
min	2.50
25%	13.50
50%	35.00
75%	61.81
max	5,000.00

Instead of breaking df_org_spending into chunks by value, we'll simply divide it up evenly into 7 parts (5 wasn't enough for previous plot):

First, sort the DataFrame by value high to low...

df_org_spending_mean.sort_values(
    by='Average_Spending', inplace=True, ascending=False)

df_org_spending_mean

	Organisation	Average_Spending
23	RVSN USSR	5,000.00
20	NASA	514.37
3	Boeing	177.29
1	Arianespace	160.76
31	ULA	157.30
13	ILS	101.54
18	MHI	92.78
19	Martin Marietta	80.16
29	SpaceX	63.98
32	US Air Force	59.65
21	Northrop	49.67
15	JAXA	49.20
8	Eurockot	41.80
33	VKS RF	40.35
4	CASC	38.50
7	ESA	37.00
26	Roscosmos	36.98
17	Lockheed	35.00
14	ISRO	33.64
16	Kosmotras	29.00
22	OrienSpace	27.22
30	Starsem	25.00
11	GK LS	23.00
6	EER	20.00
10	Firefly	15.00
27	Sandia	15.00
0	ABL SS	12.00
24	Relativity Space	12.00
34	Virgin Orbit	12.00
28	Space One	9.00
9	ExPace	8.61
25	Rocket Lab	7.50
5	CASIC	5.80
12	Galactic Energy	4.38
2	Astra	2.50

mean_01 = df_org_spending_mean[df_org_spending_mean["Average_Spending"] >= 180]

mean_02 = df_org_spending_mean[(df_org_spending_mean["Average_Spending"] < 180) & (
    df_org_spending_mean["Average_Spending"] >= 60)]

mean_03 = df_org_spending_mean[(df_org_spending_mean["Average_Spending"] < 60) & (
    df_org_spending_mean["Average_Spending"] >= 36)]

mean_04 = df_org_spending_mean[(df_org_spending_mean["Average_Spending"] < 36) & (
    df_org_spending_mean["Average_Spending"] >= 13)]

mean_05 = df_org_spending_mean[df_org_spending_mean["Average_Spending"] < 13]

Now create a plot similar to the previous:

average_ranges = [(mean_01, "Most Average Spending"),
                  (mean_02, "2nd Most Average Spending"),
                  (mean_03, "Median Average Spending"),
                  (mean_04, "2nd Least Average Spending"),
                  (mean_05, "Least Average Spending")]

fig, axes = plt.subplots(nrows=len(spending_ranges), figsize=(29, 23))

for (df, title), ax in zip(average_ranges, axes):
    ax.bar(df['Organisation'],
           df['Average_Spending'],
           color=choices(color_list, k=len(df)))
    ax.set_title(title, size=24, weight='bold')

plt.subplots_adjust(
    bottom=0.1,
    top=0.9,
    hspace=0.5,
)

plt.suptitle(t="Space Agency Averyage Spending on Rocket Launches 1957-2024\n1 Unit: $1,000,000 (US)",
             size=36, weight='bold')

plt.rcParams.update({
    'grid.color': 'gainsboro',
    'axes.facecolor': 'mintcream',
})

plt.show()

---

---

Chart the Number of Launches per Year

Add 'Year' column.

df_data["Year"] = df_data["Date"].dt.year

df_data.sample()

	Organisation	Location	Country_Name	ISO_Code	Date	Detail	Rocket_Status	Price_US_Mil	Mission_Status	Year
4056	RVSN USSR	Site 31/6, Baikonur Cosmodrome, Kazakhstan	Kazakhstan	KAZ	1971-05-06 06:20:00+00:00	Voskhod | Cosmos 410	StatusRetired	NaN	Success	1971

Re-order columns so date info is together:

df_data.columns

Index(['Organisation', 'Location', 'Country_Name', 'ISO_Code', 'Date',
       'Detail', 'Rocket_Status', 'Price_US_Mil', 'Mission_Status', 'Year'],
      dtype='object')

df_data = df_data[['Organisation',
                   'Location',
                   'Country_Name',
                   'ISO_Code',
                   'Date',
                   'Year',
                   'Detail',
                   'Rocket_Status',
                   'Price_US_Mil',
                   'Mission_Status']]

Verify addition of new columns:

df_data.head()

	Organisation	Location	Country_Name	ISO_Code	Date	Year	Detail	Rocket_Status	Price_US_Mil	Mission_Status
0	SpaceX	SLC-40, Cape Canaveral SFS, Florida, USA	United States of America	USA	2024-04-28 22:08:00+00:00	2024	Falcon 9 Block 5 | Starlink Group 6-54	StatusActive	67.00	Success
1	SpaceX	LC-39A, Kennedy Space Center, Florida, USA	United States of America	USA	2024-04-28 00:34:00+00:00	2024	Falcon 9 Block 5 | Galileo FOC FM25 & FM27	StatusActive	67.00	Success
2	CASC	Site 901 (SLS-1), Jiuquan Satellite Launch Cen...	China	CHN	2024-04-25 12:59:00+00:00	2024	Long March 2F/G | Shenzhou 18	StatusActive	NaN	Success
3	Rocket Lab	Rocket Lab LC-1B, Mahia Peninsula, New Zealand	New Zealand	NZL	2024-04-23 22:32:00+00:00	2024	Electron/Curie | Beginning Of The Swarm	StatusActive	7.50	Success
4	SpaceX	SLC-40, Cape Canaveral SFS, Florida, USA	United States of America	USA	2024-04-23 22:17:00+00:00	2024	Falcon 9 Block 5 | Starlink Group 6-53	StatusActive	67.00	Success

Create DF which adds and groups years:

df_year = df_data.groupby('Year').size().reset_index(name='Count')

Plot by year:

df_year_min = df_year.Year.min()
df_year_max = df_year.Year.max()

x_ticks = [i for i in range(df_year_min, df_year_max + 1, 5)]

# Remove last year since it's not a complete year's data:
df_year_not_last = df_year.drop(df_year.index[-1])

fig, ax = plt.subplots(figsize=(23, 17))

x_data = df_year_not_last["Year"]
y_data = df_year_not_last["Count"]

# Make bars go snug against x-boundaries:
x_min_limit = df_year_not_last["Year"].min() - .5
x_max_limit = df_year_not_last["Year"].max() + .5

cmap = plt.get_cmap('cividis')
normalize = plt.Normalize(vmin=x_min_limit, vmax=x_max_limit)
colors = [cmap(normalize(value)) for value in x_data]

ax.bar(x_data,
       y_data,
       width=.75,
       color=colors,
       edgecolor='lemonchiffon',
       linewidth=2)


ax.set_xlim([x_min_limit, x_max_limit])

x_min = df_year_not_last["Year"].min()
x_max = df_year_not_last["Year"].max()
x_ticks = [i for i in range(x_min, x_max + 1)]
ax.xaxis.set_ticks(x_ticks)

y_ticks = [i for i in range(0, 250, 10)]
ax.yaxis.set_ticks(y_ticks)

ax.set_xlabel("Year", size=24, weight='bold')
ax.set_ylabel("Number of Launches", size=24, weight='bold')

plt.xticks(rotation=90)
plt.title(f"All Rocket Launches per Year, 1957-2023", size=32, weight='bold')

plt.show()

Chart the Number of Launches Month-on-Month until the Present

Which month has seen the highest number of launches in all time? Superimpose a rolling average on the month on month time series chart.

Create column based on months:

month_year = df_data['Date'].dt.strftime('%Y-%m')

df_data.insert(6, 'Month', month_year)

Add a column which counts number of launches for each month:

month_count = df_data.groupby('Month')['Month'].transform('count')
df_data.insert(7, 'Month_Count', month_count)

Ensure conversion to DT objects and set timezone to UTC:

df_data['Month'] = pd.to_datetime(
    df_data['Month'] + '-01').dt.tz_localize('UTC')

Imports:

# Register converters to avoid warnings:
from pandas.plotting import register_matplotlib_converters

register_matplotlib_converters()

import matplotlib.dates as mdates

years = mdates.YearLocator(base=5)
months = mdates.MonthLocator()
years_fmt = mdates.DateFormatter('%Y')

fig, ax = plt.subplots(figsize=(11, 7), dpi=120)

ax.set_title("Number of Launches by Month 1957-Present:\nMonth to Month and Rolling Average",
             fontsize=18, weight='bold')

ax.tick_params(axis='both', labelsize=10)

ax.set_xlabel('Month', fontsize=14, weight='bold')
ax.set_ylabel('Number of Launches', fontsize=14, weight='bold')

plt.xticks(fontsize=14, rotation=45)
plt.yticks(fontsize=14)

ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(years_fmt)
ax.xaxis.set_minor_locator(months)

ax.set_xlim([df_data.Date.min(), df_data.Date.max()])
ax.set_ylim([0, 40])

ax.grid(color='gainsboro', linestyle='--')

ax.fill_between(
    df_data.Month,
    df_data.Month_Count,
    label="Launches per Month",
    color='darkorange',
    alpha=.45,
)

# Get rolling average over 12 months:
rolling_window = 60
rolling_window_year = round(rolling_window / 12, 2)
month_rolling = df_data['Month_Count'].rolling(window=rolling_window).mean()

ax.plot(
    df_data.Month,
    month_rolling,
    label=f"{rolling_window_year} Year Rolling Average",
    color='navy',
    alpha=0.5,
)

plt.grid('off')
plt.legend()
plt.show()

---

---

Launches per Month: Which months are most popular and least popular for launches?

Some months have better weather than others. Which time of year seems to be best for space missions?

df_month = df_data.groupby('Month').size().reset_index(name='Count')

Rename 'Month' to differentiate from what we're about to create:

df_data.rename(columns={'Month': 'Month_DT_01'}, inplace=True)

Create column with only Month names:

month_name = df_data['Date'].dt.strftime('%B')

df_data.insert(7, 'Month_Name', month_name)

df_month_name = df_data.groupby('Month_Name').size().reset_index(name='Count')

df_month_name

	Month_Name	Count
0	April	441
1	August	433
2	December	540
3	February	380
4	January	322
5	July	397
6	June	448
7	March	422
8	May	371
9	November	401
10	October	444
11	September	427

Plot the data:

# Define variables:
month_names = df_month_name.Month_Name
month_counts = df_month_name.Count

from matplotlib import colormaps

seaborn_palettes = ['deep', 'muted', 'pastel', 'bright', 'dark', 'colorblind']

all_palettes = list(colormaps) + seaborn_palettes

# Initialize MatPlotLib figure
fig, ax = plt.subplots(figsize=(13, 7))

# palette = choice(all_palettes)
# I like Seaborn's palettes for this plot:
palette = choice(seaborn_palettes)

# Plot the months/counts:
sns.barplot(x=month_names, y=month_counts, hue=month_names, palette=palette)

plt.xlabel("Month Name", size=14, weight='bold')
plt.ylabel("Number of Launches", size=14, weight='bold')

plt.title(f"Rocket Launches by Month of the Year\n(color palette: {palette})",
          size=20,
          weight='bold')

plt.show()

Analysis:

December is the most popular, January the least.

---

---

How has the Launch Price varied Over Time?

Create a line chart that shows the average price of rocket launches over time.

To keep the plot uncluttered, I'll plot by year.

df_data.sample()

	Organisation	Location	Country_Name	ISO_Code	Date	Year	Month_DT_01	Month_Name	Month_Count	Detail	Rocket_Status	Price_US_Mil	Mission_Status
1708	ILS	SLC-36A, Cape Canaveral AFS, Florida, USA	United States of America	USA	2004-04-16 00:45:00+00:00	2004	2004-04-01 00:00:00+00:00	April	4	Atlas IIAS | Superbird 6	StatusRetired	NaN	Success

Create DF with Year and Price:

df_price_year = df_data.filter(['Year', 'Price_US_Mil'], axis=1).sort_values(by='Year')

Drop NaN values:

df_price_year.dropna(inplace=True)

Drop duplicates:

df_price_year.drop_duplicates(inplace=True)

Reset index:

df_price_year.reset_index(inplace=True, drop=True)

Ensure conversion to DT objects and set timezone to UTC:

df_price_year['Year'] = pd.to_datetime(
    df_price_year['Year'].astype(str), format='%Y')

Verify new DF:

Use dates/register imports again:

import matplotlib.dates as mdates
from pandas.plotting import register_matplotlib_converters

register_matplotlib_converters()
years = mdates.YearLocator(base=5)
years_fmt = mdates.DateFormatter('%Y')

fig, ax = plt.subplots(figsize=(11, 7), dpi=120)

ax.set_title("Price by Year: All Rocket Launches 1957-Present\n$Millions (US)",
             fontsize=18, weight='bold')

ax.tick_params(axis='both', labelsize=10)

ax.set_xlabel('Year', fontsize=14, weight='bold')
ax.set_ylabel('Rocket Price', fontsize=14, weight='bold')

plt.xticks(fontsize=14, rotation=45)
plt.yticks(fontsize=14)

ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(years_fmt)

ax.set_xlim([df_price_year.Year.min(), df_price_year.Year.max()])
ax.set_ylim([df_price_year.Price_US_Mil.min(),
            df_price_year.Price_US_Mil.max()])

ax.grid(color='gainsboro', linestyle='--')

ax.plot(
    df_price_year.Year,
    df_price_year.Price_US_Mil,
    label="Launches per Year",
    color='darkorange',
    linestyle='dashed',
)

plt.grid('off')
plt.legend()
plt.show()

Analysis:

It looks like the price skyrocketed in the mid-80's.

If I recall that was the time of the most Space Shuttle launches.

---

---

Chart the Number of Launches over Time by the Top 10 Organisations.

How has the dominance of launches changed over time between the different players?

# Get top 10 organizations in DF as list:
org_names_top_10_list = df_data['Organisation'].value_counts().nlargest(
    10).index

# Filter to include only top 10 orgs:
df_org_top_10 = df_data[(df_data['Organisation'].isin(org_names_top_10_list))]

Custom colors I like for this plot:

colors_10 = ['black', 'darkgrey', 'fuchsia', 'firebrick', 'peru',
             'gold', 'forestgreen', 'lime', 'dodgerblue', 'darkorchid']

linestyles = ['-', '--', '-.', ':']

from random import randint

fig, ax = plt.subplots(figsize=(11, 8))

ind = 0

for org, data in df_org_top_10.groupby('Organisation'):
    org_data = data.groupby('Year').size()
    plt.plot(org_data.index,
             org_data.values,
             label=org,
             color=colors_10[ind],
             linewidth=2,
             linestyle=choice(linestyles))
    ind += 1

ax.set_xlim([1957, 2023])

ax.set_title("Top 10 Space Agencies:\nNumber of Rocket Launches 1957-2023",
             fontsize=18,
             weight='bold')
ax.set_xlabel("Year", fontsize=14, weight='bold')
ax.set_ylabel("Number of Rocket Launches", fontsize=14, weight='bold')

ax.legend(title="Space Agency", title_fontsize='large')

plt.show()

---

---

Cold War Space Race: USA vs USSR

The cold war lasted from the start of the dataset up until 1991.

Create DF with USA and USSR:

# Check names of countries to wrangle former USSR countries into one group:
df_data.Country_Name.unique()

array(['United States of America', 'China', 'New Zealand',
       'Russian Federation', 'Kazakhstan', 'Japan', 'India',
       'Iran, Islamic Republic of', 'Korea, Republic of',
       "Korea, Democratic People's Republic of", 'France', 'Israel',
       'United Kingdom of Great Britain and Northern Ireland', 'Brazil',
       'Kenya', 'Australia'], dtype=object)

# The two former Soviet republics:
ussr = ['Russian Federation', 'Kazakhstan']
# Create DF combining these two values into `df_ussr`:
df_ussr = df_data[df_data['Country_Name'].isin(ussr)]
# Rename column to USSR:
df_ussr.loc[:, 'Country_Name'] = 'USSR'

# Create DF combining these two values into `df_usa`:
df_usa = df_data[df_data['Country_Name'] == 'United States of America']
# Rename column to USA:
df_usa.loc[:, 'Country_Name'] = 'USA'

Instead of separate DF's, we can do it all in one:

# Alternatively, change country names in original DataFrame:
#  (maybe we can use both this and the individual DataFrames from before)
ussr = ['Russian Federation', 'Kazakhstan']

df_usa_ussr = df_data.copy()

df_usa_ussr.loc[df_usa_ussr['Country_Name'].isin(
    ussr), 'Country_Name'] = 'USSR'

df_usa_ussr.loc[df_usa_ussr['Country_Name'] ==
                'United States of America', 'Country_Name'] = 'USA'

# Now, filter out all the other countries:
df_usa_ussr = df_usa_ussr[df_usa_ussr['Country_Name'].isin(['USA', 'USSR'])]

# Now let's remove all dates after 1991:
df_usa_ussr = df_usa_ussr[df_usa_ussr['Year'] <= 1991]

# And reset the index:
df_usa_ussr.reset_index(inplace=True, drop=True)

---

Create a Plotly Pie Chart comparing the total number of launches of the USSR and the USA

Hint: Remember to include former Soviet Republics like Kazakhstan when analysing the total number of launches.

# Create series w/value_counts:
usa_ussr_counts = df_usa_ussr['Country_Name'].value_counts()

fig = px.pie(names=usa_ussr_counts.index,
             values=usa_ussr_counts,
             labels=usa_ussr_counts.index,
             width=1000,
             height=1000)

title = "USA vs USSR Space Agency Launches:\nCold War Era (1957-1991)"

fig.update_layout(title=dict(text=title,
                             font=dict(size=22),
                             automargin=True,
                             yref='paper'))

fig.update_traces(textposition='inside',
                  textinfo='value+percent+label')

fig.show()

Analysis:

It appears the USSR had a lot more launches during the Cold War.

---

colors_varied = ['black', 'darkgrey', 'fuchsia', 'firebrick', 'peru',
                 'gold', 'forestgreen', 'lime', 'dodgerblue', 'darkorchid',
                 "b", "g", "r", "c", "m", "y", "k", "blueviolet", "brown",
                 "burlywood", "cadetblue", "chartreuse", "chocolate", "coral",
                 "cornflowerblue", "mediumblue", "mediumorchid", "mediumpurple",
                 "mediumseagreen",]

print(sorted(list(set(colors_varied))))

['b', 'black', 'blueviolet', 'brown', 'burlywood', 'c', 'cadetblue', 'chartreuse', 'chocolate', 'coral', 'cornflowerblue', 'darkgrey', 'darkorchid', 'dodgerblue', 'firebrick', 'forestgreen', 'fuchsia', 'g', 'gold', 'k', 'lime', 'm', 'mediumblue', 'mediumorchid', 'mediumpurple', 'mediumseagreen', 'peru', 'r', 'y']

Create a Chart that Shows the Total Number of Launches Year-On-Year by the Two Superpowers

from random import uniform

fig, ax = plt.subplots(figsize=(17, 9))


# Soviet Red, Old Glory Blue:
red_blue = ['#002868', '#c60000']
# Solid and dotted:
matplotlib_linestyles = ['-', ':']
ind = 0

# Vary the linewidths and linestyles randomly:
for country, data in df_usa_ussr.groupby('Country_Name'):
    country_data = data.groupby('Year').size()
    plt.plot(country_data.index,
             country_data.values,
             label=country,
             linewidth=round(uniform(3, 6), 2),  # For variation
             linestyle=matplotlib_linestyles[ind],
             color=red_blue[ind])
    ind += 1

ax.set_xlim([1957, 1991])

ax.set_title("USA vs USSR Cold War Space Agencies:\nYearly Rocket Launches 1957-1991",
             fontsize=24,
             weight='bold')

ax.set_xlabel("Year", fontsize=14, weight='bold')
ax.set_ylabel("Number of Rocket Launches", fontsize=14, weight=14)

ax.legend(title="Cold War Superpower", title_fontsize='large')

plt.show()

Analysis:

Looks like the Soviets had a lot more launchesafter the early 60's, the US had a lot more until that point.

---

Chart the Total Number of Mission Failures Year on Year.

df_usa_ussr.Mission_Status.unique()

array(['Success', 'Failure', 'Partial Failure', 'Prelaunch Failure'],
      dtype=object)

failure_types = ['Success', 'Failure', 'Partial Failure', 'Prelaunch Failure']

df_cold_war_failure = df_usa_ussr[df_usa_ussr['Mission_Status'].isin(
    failure_types)]

Modify the previous plot to show Mission Failures:

from random import uniform

fig, ax = plt.subplots(figsize=(17, 9))


# Soviet Red, Old Glory Blue:
red_blue = ['#002868', '#c60000']
# Solid and dotted:
matplotlib_linestyles = ['-', ':']
ind = 0

for country, data in df_cold_war_failure.groupby('Country_Name'):
    country_data = data.groupby('Year').size()
    plt.plot(country_data.index,
             country_data.values,
             label=country,
             linewidth=round(uniform(3, 6), 2),
             linestyle=matplotlib_linestyles[ind],
             color=red_blue[ind])
    ind += 1

ax.set_xlim([1957, 1991])

title = "USA vs USSR Cold War Space Agencies:\nYearly Rocket Launch Failures 1957-1991\nIncludes 'Failures', 'Partial Failures' and 'Prelaunch Failures'"

ax.set_title(title,
             fontsize=24,
             weight='bold')

ax.set_xlabel("Year", fontsize=14, weight='bold')
ax.set_ylabel("Number of Rocket Launch Failures", fontsize=14, weight=14)

ax.legend(title="Cold War Superpower", title_fontsize='large')

plt.show()

Analysis:

From the plot, it looks like the US had more failures early on, then things evened out.

It appears the USSR was better than the US at first, then by the late 60's had a dramatic increase in Mission Failures, then things evened out by the late 70's.

From the late 70's through 1991, the US and USSR both had more consistent success, but the US still had about half to one-third as many failures.

---

Chart the Percentage of Failures over Time

Did failures go up or down over time? Did the countries get better at minimising risk and improving their chances of success over time?

Create a DF which counts the number of total missions per year:

df_cold_missions = df_usa_ussr.groupby(
    ['Country_Name', 'Year']).size().reset_index(name='Total_Missions')

Create another DF which counts the successes per year for each country:

df_cold_successes = df_usa_ussr[df_usa_ussr['Mission_Status'] == 'Success'].groupby(
    ['Country_Name', 'Year']).size().reset_index(name='Success_Count')

Merge the total missions and successes DataFrames:

df_cold_merged = df_cold_missions.merge(
    df_cold_successes, on=['Country_Name', 'Year'], how='left')

Ensure years without successes have 0 instead of NaN:

df_cold_merged['Success_Count'] = df_cold_merged['Success_Count'].fillna(0)

Now add a column for success percentage:

success_ratio = (df_cold_merged.Success_Count / df_cold_merged.Total_Missions)

df_cold_merged['Success_Percentage'] = success_ratio * 100

Sort by year for clarity of chronology in viewing DF:

df_cold_merged.sort_values(by=['Year'], inplace=True)

Check the DF:

df_cold_merged

	Country_Name	Year	Total_Missions	Success_Count	Success_Percentage
0	USA	1957	1	0.00	0.00
35	USSR	1957	2	2.00	100.00
1	USA	1958	23	5.00	21.74
36	USSR	1958	5	1.00	20.00
2	USA	1959	16	6.00	37.50
...	...	...	...	...	...
32	USA	1989	16	16.00	100.00
68	USSR	1990	37	35.00	94.59
33	USA	1990	26	25.00	96.15
34	USA	1991	18	16.00	88.89
69	USSR	1991	29	27.00	93.10

70 rows × 5 columns

Plot percentage:

I first tried plotting everything on one axis, but that obscured the different y-axis scales:

from random import uniform

fig, ax = plt.subplots(figsize=(17, 9))

red_blue = ['#002868', '#c60000']
matplotlib_linestyles = ['-', ':', '-', ':']
color_ind = 0
line_ind = 0

for country, data in df_cold_merged.groupby('Country_Name'):
    plt.plot(data.Year,
             data.Success_Percentage,
             label=f"{country} Success Percentage",
             linewidth=round(uniform(3, 6), 2),
             linestyle=matplotlib_linestyles[line_ind],
             color=red_blue[color_ind])
    line_ind += 1
    plt.plot(data.Year,
             data.Total_Missions,
             label=f"{country} Mission Total",
             linewidth=round(uniform(3, 6), 2),
             linestyle=matplotlib_linestyles[line_ind],
             color=red_blue[color_ind])
    color_ind += 1

ax.set_xlim([1957, 1991])

title = "USA vs USSR Cold War Space Agencies 1957-1991:\nNumber of Launches and Percentage Success\n"

ax.set_title(title,
             fontsize=24,
             weight='bold')

ax.set_xlabel("Year", fontsize=14, weight='bold')
ax.set_ylabel("Number of Rocket Launch Failures", fontsize=14, weight=14)

ax.legend(title="Cold War Superpower", title_fontsize='large')

plt.show()

So I decided on a dual-axis, one for Number of Missions, one for Percentage Success:

from random import uniform

fig, ax1 = plt.subplots(figsize=(17, 9))
ax2 = ax1.twinx()

red_blue = ['#002868', '#c60000']
dashed = ['--', ':']
color_ind = 0


for country, data in df_cold_merged.groupby('Country_Name'):
    ax1.plot(data.Year,
             data.Total_Missions,
             label=f"{country} Number of Missions",
             linewidth=round(uniform(2, 5), 2),
             linestyle='-',
             color=red_blue[color_ind])
    ax2.plot(data.Year,
             data.Success_Percentage,
             label=f"{country} Success Percentage",
             linewidth=round(uniform(2, 4), 2),
             linestyle=choice(dashed),
             color=red_blue[color_ind], alpha=.7)
    color_ind += 1

ax1.set_xlim([1957, 1991])

title = "USA vs USSR Cold War Space Agencies 1957-1991:"
title += "\nNumber of Launches and Percentage Success"

ax1_color = 'gainsboro'
ax1.set_title(title, fontsize=24, weight='bold')

ax1.set_xlabel("Year", fontsize=14, weight='bold')
ax1.set_ylabel("Number of Rocket Launch Failures",
               fontsize=14,
               weight='bold',
               color=ax1_color)
ax1.grid(color=ax1_color, linestyle='-')

ax2_color = 'grey'
ax2.set_ylabel("Success Percentage",
               fontsize=14,
               weight='bold',
               color=ax2_color)
ax2.grid(color=ax2_color, linestyle=':')

lines, labels = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2,
           labels + labels2,
           title="Cold War Superpower",
           title_fontsize='large')

# Alternatively, make legends separately:
# ax1.legend(title="Number of Missions", title_fontsize='large', loc='upper left')
# ax2.legend(title="Success Percentage", title_fontsize='large', loc='upper right')

plt.show()

Analysis:

USA had more launches in the early 1960's.

USSR had a lot more launches in the 1970's.

Both nations had steady launch amounts in the 1980's, with USSR averaging about 40 a year and the USA under 20 during this time.

The success rates were poor in the beginning for both nations.

Then the US and USSR maintained about 80-90% success rates subsequently, although the USA had a few noticable dips in the succes rate throughout the time while maintaining this average.

USSR had a steep decline in launch number in the late 1970's and never achieved the same level of launches after that. I cannot say whether that's a good or bad thing for them -- perhaps fewer launches means more focused, competent projects?

A very descriptive plot, all in all!

---

---

For Every Year Show which Country was in the Lead in terms of Total Number of Launches (up to and including including 2020)

Do the results change if we only look at the number of successful launches?

Create DataFrame which groups entries by Country/Year size for years before and including 2020:

# Assert two columns in `.groupby()` params to for granular control of aggregated data:
df_country_year = df_data.groupby(
    ['Country_Name', 'Year']).size().reset_index(name='Yearly_Launches')

Order the DF by year for clarity in chronology:

df_country_year.sort_values(by=['Year'], inplace=True)

df_country_year = df_country_year[df_country_year['Year'] <= 2020]

Get the indices with the max number of entries for each year w/equation which asserts the .transform() method:

ind_eq1 = df_country_year.groupby('Year')['Yearly_Launches'].transform('max')
ind_eq2 = df_country_year['Yearly_Launches']

indices = ind_eq1 == ind_eq2

Use the indices to filter the DF to get country names:

df_country_year_max = df_country_year[indices]

Reset the index for clarity and consistency, drop=True to not create new index column:

df_country_year_max.reset_index(inplace=True, drop=True)

View the results:

df_country_year_max

	Country_Name	Year	Yearly_Launches
0	Kazakhstan	1957	2
1	United States of America	1958	23
2	United States of America	1959	16
3	United States of America	1960	30
4	United States of America	1961	43
...	...	...	...
60	United States of America	2016	27
61	United States of America	2017	30
62	China	2018	39
63	China	2019	34
64	United States of America	2020	42

65 rows × 3 columns

color_list = ['black', 'blue', 'brown', 'chocolate', 'cornflowerblue', 'crimson',
              'darkblue', 'darkcyan', 'darkgoldenrod', 'darkgreen', 'darkkhaki',
              'darkmagenta', 'darkolivegreen', 'darkorange', 'darkorchid', 'darkred',
              'darksalmon', 'darkseagreen', 'darkslateblue', 'darkslategray',
              'darkslategrey', 'darkturquoise', 'darkviolet', 'deeppink', 'dimgray',
              'dodgerblue', 'firebrick', 'forestgreen', 'goldenrod', 'green', 'greenyellow', 'indigo', 'lightgreen', 'limegreen', 'maroon', 'mediumblue',
              'mediumseagreen', 'mediumslateblue', 'mediumspringgreen', 'midnightblue',
              'navy', 'olive', 'olivedrab', 'palegreen', 'peru', 'purple', 'rebeccapurple', 'rosybrown', 'royalblue', 'saddlebrown', 'sandybrown',
              'seagreen', 'sienna', 'slateblue', 'springgreen', 'steelblue', 'yellowgreen']

bar_colors = choices(color_list, k=len(df_country_year_max.Year))

Concatenate Country_Name and Year for the label:

zip_year_country = zip(df_country_year_max.Year,
                       df_country_year_max.Country_Name,
                       df_country_year_max.Yearly_Launches)

labels_year_country = [
    f"{year}: {country} ({num})" for country, year, num in zip_year_country
]

fig, ax = plt.subplots(figsize=(13, 11))

ax.bar(x=df_country_year_max.Year,
       height=df_country_year_max.Yearly_Launches,
       label=labels_year_country,
       color=bar_colors)

ax.set_title("Country Launch Leader by Year", fontsize=36, weight='bold')
ax.set_xlabel("Country", fontsize=18)
ax.set_ylabel("Number of Launches", fontsize=18)

plt.legend(ncol=2,
           title="Country: Year (Number of Launches)",
           fontsize='medium',
           bbox_to_anchor=(1.05, 1))

plt.xticks(rotation=90)

plt.show()

To answer the question:

Do the results change if we only look at the number of successful launches?

Let's now create a DF for Country Launcher Leader by Year for successful launches only.

Repeat the exact same process as above but filtering the DF by column: Mission_Status: Success...

# Create DF filtering for Mission_Status == Success:
df_success = df_data[df_data['Mission_Status'] == 'Success']

# Group by country name then year, create those columns and another for launch count:
df_success_year = df_data.groupby(
    ['Country_Name', 'Year']).size().reset_index(name='Yearly_Launches')


df_success_year.sort_values(by=['Year'], inplace=True)

df_success_year = df_success_year[df_success_year['Year'] <= 2020]

# Get indices for max entry per year and create DF for max counts:
ind_eq1 = df_success_year.groupby('Year')['Yearly_Launches'].transform('max')
ind_eq2 = df_success_year['Yearly_Launches']
indices = ind_eq1 == ind_eq2
df_success_year_max = df_success_year[indices]
df_success_year_max.reset_index(inplace=True, drop=True)
df_success_year_max

# Clear colors to display on white background
color_list = ['black', 'blue', 'brown', 'chocolate', 'cornflowerblue', 'crimson',
              'darkblue', 'darkcyan', 'darkgoldenrod', 'darkgreen', 'darkkhaki',
              'darkmagenta', 'darkolivegreen', 'darkorange', 'darkorchid', 'darkred',
              'darksalmon', 'darkseagreen', 'darkslateblue', 'darkslategray',
              'darkslategrey', 'darkturquoise', 'darkviolet', 'deeppink', 'dimgray',
              'dodgerblue', 'firebrick', 'forestgreen', 'goldenrod', 'green', 'greenyellow', 'indigo', 'lightgreen', 'limegreen', 'maroon', 'mediumblue',
              'mediumseagreen', 'mediumslateblue', 'mediumspringgreen', 'midnightblue',
              'navy', 'olive', 'olivedrab', 'palegreen', 'peru', 'purple', 'rebeccapurple', 'rosybrown', 'royalblue', 'saddlebrown', 'sandybrown',
              'seagreen', 'sienna', 'slateblue', 'springgreen', 'steelblue', 'yellowgreen']

# Pick the amount of colors for the length of the DF:
bar_colors = choices(color_list, k=len(df_success_year_max.Year))

# Create descriptive labels for legend:
zip_year_country = zip(df_success_year_max.Year,
                       df_success_year_max.Country_Name,
                       df_success_year_max.Yearly_Launches)

labels_year_country = [f"{year}: {country} ({num})" for country, year, num in zip_year_country]

# Now plot and see if there's a difference when we plot based only on Successes:
fig, ax = plt.subplots(figsize=(13, 11))

ax.bar(x=df_success_year_max.Year,
       height=df_success_year_max.Yearly_Launches,
       label=labels_year_country,
       color=bar_colors)

ax.set_title("Country Launch Leader by Year:\nSuccessful Launches Only", fontsize=36, weight='bold')
ax.set_xlabel("Country", fontsize=18)
ax.set_ylabel("Number of Launches", fontsize=18)

plt.legend(ncol=2,
           title="Country: Year (Number of Launches)",
           fontsize='medium',
           bbox_to_anchor=(1.05, 1))

plt.xticks(rotation=90)

plt.show()

It's hard to analyze quickly by looking at the plots, let's just print out the results for the all-inclusive DF and the successes DF in pairs:

differences = 0

for i in range(len(df_success_year_max.Country_Name)):
    year_all = df_country_year_max.Year[i]
    country_all = df_country_year_max.Country_Name[i]
    year_success = df_success_year_max.Year[i]
    country_success = df_success_year_max.Country_Name[i]

    # # Print all the info:
    # print(f"{year_all}: {country_all} (all launches leader)")
    # print(f"{year_success}: {country_success} (successful launches leader)")

    # # Print just the difference:
    if country_all != country_success:
        differences += 1
        print(f"{year_all}: {country_all} (all launches leader)")
        print(f"{year_success}: {country_success} (successful launches leader)")

print(f"There were {differences} year(s) with different results.")

There were 0 year(s) with different results.

Nope -- the data remains the same.

We can try the same thing below for Organisations, after the initial operation...

---

---

Create a Year-on-Year Chart Showing the Organisation Doing the Most Number of Launches

Which organisation was dominant in the 1970s and 1980s? Which organisation was dominant in 2018, 2019 and 2020?

Just do what we did above for countries, but with organisations:

df_org_year = df_data.groupby(
    ['Organisation', 'Year']).size().reset_index(name='Yearly_Launches')

df_org_year.sort_values(by=['Year'], inplace=True)

df_org_year = df_org_year[df_org_year['Year'] <= 2020]

ind_eq1 = df_org_year.groupby('Year')['Yearly_Launches'].transform('max')
ind_eq2 = df_org_year['Yearly_Launches']
indices = ind_eq1 == ind_eq2

df_org_year_max = df_org_year[indices]

df_org_year_max.reset_index(inplace=True, drop=True)

Verify the result:

df_org_year_max

	Organisation	Year	Yearly_Launches
0	RVSN USSR	1957	2
1	US Navy	1958	12
2	US Air Force	1959	10
3	US Air Force	1960	21
4	US Air Force	1961	23
...	...	...	...
66	CASC	2016	22
67	SpaceX	2017	18
68	CASC	2018	37
69	CASC	2019	27
70	CASC	2020	34

71 rows × 3 columns

color_list = ['black', 'blue', 'brown', 'chocolate', 'cornflowerblue', 'crimson',
              'darkblue', 'darkcyan', 'darkgoldenrod', 'darkgreen', 'darkkhaki',
              'darkmagenta', 'darkolivegreen', 'darkorange', 'darkorchid', 'darkred',
              'darksalmon', 'darkseagreen', 'darkslateblue', 'darkslategray',
              'darkslategrey', 'darkturquoise', 'darkviolet', 'deeppink', 'dimgray',
              'dodgerblue', 'firebrick', 'forestgreen', 'goldenrod', 'green', 'greenyellow', 'indigo', 'lightgreen', 'limegreen', 'maroon', 'mediumblue',
              'mediumseagreen', 'mediumslateblue', 'mediumspringgreen', 'midnightblue',
              'navy', 'olive', 'olivedrab', 'palegreen', 'peru', 'purple', 'rebeccapurple', 'rosybrown', 'royalblue', 'saddlebrown', 'sandybrown',
              'seagreen', 'sienna', 'slateblue', 'springgreen', 'steelblue', 'yellowgreen']

bar_colors = choices(color_list, k=len(df_org_year_max.Year))

Concatenate Country_Name and Year for the label:

zip_year_org = zip(df_org_year_max.Year,
                   df_org_year_max.Organisation,
                   df_org_year_max.Yearly_Launches)

labels_year_org = [f"{year}: {org} ({num})" for org, year, num in zip_year_org]

fig, ax = plt.subplots(figsize=(13, 11))

ax.bar(x=df_org_year_max.Year,
       height=df_org_year_max.Yearly_Launches,
       label=labels_year_org,
       color=bar_colors)

ax.set_title("Organisation Launch Leader by Year", fontsize=36, weight='bold')
ax.set_xlabel("Organisation", fontsize=18)
ax.set_ylabel("Number of Launches", fontsize=18)

plt.legend(ncol=2,
           title="Organisation: Year (Number of Launches)",
           fontsize='medium',
           bbox_to_anchor=(1.05, 1))

plt.xticks(rotation=90)

plt.show()

Answering Angela's questions:

Which organisation was dominant in the 1970s and 1980s?

• RVSN USSR

Which organisation was dominant in 2018, 2019 and 2020?

• CASC

Now to check if there is a difference if we analyze Organisation leaders based only on successes:

# Create DF filtering for Mission_Status == Success:
df_org_success = df_data[df_data['Mission_Status'] == 'Success']

# Group by org name then year, create those columns and another for launch count:
df_org_success_year = df_data.groupby(
    ['Organisation', 'Year']).size().reset_index(name='Yearly_Launches')


df_org_success_year.sort_values(by=['Year'], inplace=True)

df_org_success_year = df_org_success_year[df_org_success_year['Year'] <= 2020]

# Get indices for max entry per year and create DF for max counts:
ind_eq1 = df_org_success_year.groupby(
    'Year')['Yearly_Launches'].transform('max')
ind_eq2 = df_org_success_year['Yearly_Launches']
indices = ind_eq1 == ind_eq2
df_org_success_year_max = df_org_success_year[indices]
df_org_success_year_max.reset_index(inplace=True, drop=True)
df_org_success_year_max

# Clear colors to display on white background
color_list = ['black', 'blue', 'brown', 'chocolate', 'cornflowerblue', 'crimson',
              'darkblue', 'darkcyan', 'darkgoldenrod', 'darkgreen', 'darkkhaki',
              'darkmagenta', 'darkolivegreen', 'darkorange', 'darkorchid', 'darkred',
              'darksalmon', 'darkseagreen', 'darkslateblue', 'darkslategray',
              'darkslategrey', 'darkturquoise', 'darkviolet', 'deeppink', 'dimgray',
              'dodgerblue', 'firebrick', 'forestgreen', 'goldenrod', 'green', 'greenyellow', 'indigo', 'lightgreen', 'limegreen', 'maroon', 'mediumblue',
              'mediumseagreen', 'mediumslateblue', 'mediumspringgreen', 'midnightblue',
              'navy', 'olive', 'olivedrab', 'palegreen', 'peru', 'purple', 'rebeccapurple', 'rosybrown', 'royalblue', 'saddlebrown', 'sandybrown',
              'seagreen', 'sienna', 'slateblue', 'springgreen', 'steelblue', 'yellowgreen']

# Pick the amount of colors for the length of the DF:
bar_colors = choices(color_list, k=len(df_org_success_year_max.Year))

# Create descriptive labels for legend:
zip_year_org = zip(df_org_success_year_max.Year,
                   df_org_success_year_max.Organisation,
                   df_org_success_year_max.Yearly_Launches)

labels_year_org = [f"{year}: {org} ({num})" for org, year, num in zip_year_org]

# Now plot and see if there's a difference when we plot based only on Successes:
fig, ax = plt.subplots(figsize=(13, 11))

ax.bar(x=df_org_success_year_max.Year,
       height=df_org_success_year_max.Yearly_Launches,
       label=labels_year_org,
       color=bar_colors)

ax.set_title("Organisation Launch Leader by Year:\nSuccessful Launches Only", fontsize=36, weight='bold')
ax.set_xlabel("Organisation", fontsize=18)
ax.set_ylabel("Number of Launches", fontsize=18)

plt.legend(ncol=2,
           title="Organisation: Year (Number of Launches)",
           fontsize='medium',
           bbox_to_anchor=(1.05, 1))

plt.xticks(rotation=90)

plt.show()

It's hard to analyze quickly by looking at the plots, let's just print out the results for the all-inclusive DF and the successes DF in pairs:

differences = 0

for i in range(len(df_org_year_max.Organisation)):
    year_all = df_org_year_max.Year[i]
    org_all = df_org_year_max.Organisation[i]
    year_success = df_org_success_year_max.Year[i]
    org_success = df_org_success_year_max.Organisation[i]

    # # Print all the info:
    # print(f"{year_all}: {org_all} (all launches leader)")
    # print(f"{year_success}: {org_success} (successful launches leader)")

    # # Print just the difference:
    if org_all != org_success:
        differences += 1
        print(f"{year_all}: {org_all} (all launches leader)")
        print(f"{year_success}: {org_success} (successful launches leader)")

print(f"There were {differences} year(s) with different results.")

There were 0 year(s) with different results.

The results are the same, regardless of whether we include all launches of only successful launches.

Conclusion:

A very difficult project, but I got through it and completed the problems well, as far as I can see.

Lots of room for improvement on my part, but also I think I've come a long way and have a very solid grasp of concepts related to Pandas, Matplotlib, Plotly, Seaborn and other related libraries and subjects.

I used StackOverflow and other similar sites like GeeksforGeeks, as well as read, researched and studied the primary documentation.

ChatGPT was extremely helpful for helping me when I got stuck on more advanced Pandas challenges, and sometimes with plotting. I feel like I understand everything that I've written above.

Difficult day's assignment, took about a week.

Now on the the final day, almost there!

Note:

In the intro Angela uses from datetime import datetime, timedelta, I didn't end up using this...it's worth a closer look how I could have implemented these modules.